Journal of Animal Science and Technology

Korean Society of Animal Sciences and Technology (한국축산학회)
권호 표지
DOI QR코드

DOI QR Code

Effects of Number of Incomplete Data in Latest Generation on the Breeding Value Estimated by Random Regression Model

임의회귀 모형 사용시 마지막 세대의 불완전한 기록이 추정육종가에 미치는 효과

  • ;
  • ;
  • ;
  • ;
  • ;
  • ;
  • ;
  • ;
  • Salces, A.J. (National Livestock Research Institute, RDA, Korea) ;
  • Published : 2006.04.30
Download PDF
⟨ Previous Next ⟩

Abstract

The data were collected in the dairy herd improvement program from January 2000 to July 2005. Test data included 825,157 records of first parity and animals with both parents known were included. This study aimed to describe the effect of incomplete lactation records of latest generation to the change in sire's breeding value using Random Regression model (RRM) in genetic evaluation. Estimation of genetic parameter and breeding value for sire used REMLF90 and BLUPF90 program. The phenotypic value on the number of test day records between group TD11, TD8, TD5, TD2 showed no large differences. For all the group heritability of test day milk yield range from 0.30 to 0.36. However TD2 group showed low heritability the least test day recode on the latest generation. The correlation of above 50% between test day and TD11(0.610), TD8(0.616), TD5(0.661) and TD2(0.682) with different records in latest generation. Sire's rank of breeding value varied widely depending on the records on the number of lactation from start to the latest generation. Study showed that change in breeding value ranked if daughter's test recode more so it should have at least 5 test day records. The use of RRM in dairy cattle genetic evaluation would be desirable if complete lactation records for latest generation daughters of young bulls when selection for proven bulls. Random Regression model (RRM) require at least 5 test-day lactation recode.

본 연구는 현재 유전평가에 사용하는 모델보다 많은 장점을 지니고 있는 임의회귀 검정일 모형(Random regression test-day model)을 이용할 때 마지막 세대의 불완전한 검정기록이 유전능력에 어떤 영향을 주는지 알아보고자 실시하였다.이용된 재료는 유우군능력검정사업을 통하여 수집된 2000년 1월부터 2005년 6월까지의 825,157개의 초산의 검정일 자료를 이용하였으며, 유전모수와 종모우의 육종가 추정은 REMLF90, BLUPF90을 이용하였다.

Keywords

References

  1. Interbull Bulletin 26. Wiggans, G. R. Issues In defining a genetic evaluation model
  2. Jamrozik and Schaeffer, L. R. 1997. Estimates of Genetic Parameters for a Test Day Model with Random Regressions for Yield Traits of First Lactation Holsteins. J. Dairy Sci. 80: 762-770 https://doi.org/10.3168/jds.S0022-0302(97)75996-4
  3. Jamrozik Schaeffer, L. R. and Dekkers, J. C. M. 1997. Genetic Evaluation of Dairy Cattle Using Test Day Yields and Random Regression Model. J. Dairy Sci. 80: 1217-1226 https://doi.org/10.3168/jds.S0022-0302(97)76050-8
  4. Lee, D. H., Jo, J. H. and Han, K. G. 2003. Genetic parameters for milk production and somatic cell score of forst lactation in Holstein cattle with random regression test-day models. Korean J. Anim. Sci. 45(5):739-748
  5. Lee, D. H and Han, K. J. 2004. Genetic relationship between milk production, calving ease and days open at first parity in Holstein cows. Asian-Aust. J. Anim. Sci. Vol. 17, No.2: 153-158
  6. Macciotta, N. P. P., Vicario, D., Pulina, G. and Cappio-Borlino, A. 2002. Test Day and Lactation Yield Predictions in Italian Simmental Cows by ARMA Methods. J. Dairy Sci. 85: 3107-3114 https://doi.org/10.3168/jds.S0022-0302(02)74398-1
  7. Misztal, I. 2001. BLUPPF90 family pakage. http://nce.ads.uga.edu
  8. Miller, R. H., Norman, H. D., Wiggans, G. R. and Wright, J. R. 2004. Relationship of test-day somatic cell score with test-day lactation milk yields. J. Dairy Sci. 87: 2299-2306 https://doi.org/10.3168/jds.S0022-0302(04)70051-X
  9. Pool, M. H. and Meuwissen. T. H. E. 1999. Prediction of dairy milk yields from a limited number of test days using test day models. J. Dairy Sci. 82:1555-1564 https://doi.org/10.3168/jds.S0022-0302(99)75383-X
  10. Reinhardt, F., Liu, Z., Benger, A., Dopp, L. and Reents, R. 2002. Impact of Application of a Random Regression Test-day Model to Production Trait Genetic Evaluations in Dairy Cattle. INTERBULL Bulletin No.29: 103-107
  11. Schaeffer. L. R. and Dekkers. J. C. M. 1994. Random regressions in animal models for testday production on dairy cattle. Proc. 5th World Congress of Genetics Applied to Livestock Production. Guelph, Ontario, Canada X V III:443-446
  12. Swalve, H. H. 2000. Theoretical basis and computational methods for different testday genetic evaluation methods. J. Dairy Sci. 83:1115-1124 https://doi.org/10.3168/jds.S0022-0302(00)74977-0
  13. Wilmink, J. B. M. 1987. Adjustment of Test-day milk, fat, and protein yield for age, season and stage of lactation. Livest. Prod. Sci. 16, 335-348 https://doi.org/10.1016/0301-6226(87)90003-0